A/B testing pits two different copies of a page against one another. This is usually done to ensure that the tool being used to conduct the experiment is statistically sound. If the A/B test is done correctly, the tool should show no change in conversions between the control and the variation.

To see which version converts better, A/B testing sends traffic to two different pages: the control and the variation, which is a different version of the original page.

An A/A test, on the other hand, pits two identical pages against each other. An A/A test's purpose is to ensure that there is no difference between your control and variant versions, rather than finding a conversion increase.

In this comprehensive article, we will discuss in detail about A/A testing, the reasons why enterprises choose this testing, how organizations implement these tests, and so on. So let’s start by briefly understanding why companies use A/A testing.

Why do organizations make use of A/A Testing?

When a new A/B testing tool is being implemented, A/A testing is usually done. At that point, running an A/A test can help with a myriad of things, including the ones listed below:

• Verifying the accuracy of the A/B testing tool that is being used.

• Creating a conversion rate baseline for future A/B tests.

• Choosing a sample size that isn't too small.

Verifying the accuracy of the A/B Testing tool that is being used

An A/A test can be done by organizations that are going to acquire an A/B testing tool or wish to move to a new testing software to confirm that the new software works well and is set up correctly.

Before you perform an A/B test, A/A testing is a nice approach to running a sanity check. This should be done every time you start using a new tool or embark on a new project. In these circumstances, A/A testing will help determine if there is a data mismatch, such as between the number of visitors you see in your testing tool and the number of visitors you see in your web analytics tool. This also aids in the verification of your hypothesis.

A web page is A/B tested against an identical variation in an A/A test. When there is no discernible difference between the control and the variation, the outcome is anticipated to be inconclusive. There is an issue, however, when an A/A test determines a winner between two identical versions. Any of the following could be the cause:

• The tool isn't configured properly.

• The test was not carried out properly.

• The testing software is ineffective.

Creating a conversion rate baseline for future A/B tests

Before you start an A/B test, you need to know what conversion rate you'll be comparing your results against. This is your conversion rate's starting point.

An A/B test can assist you in determining your website's baseline conversion rate. Let's use an example to demonstrate this. Consider an A/A test in which the control receives 606 conversions out of 20,000 visitors, whereas variation B receives 614 conversions out of 20,000 visitors.

As a result, the conversion rate for A is 3.03%, while the conversion rate for B is 3.07%. There is no distinction between the two variants. As a result, for future A/B tests, the conversion rate benchmark can be set at 3.03–3.07%. If you do an A/B test later and the uplift is within this range, the outcome may not be quite noteworthy.

Choosing a sample size that isn't too small

Choosing an adequate sample size can be difficult. A/A testing can assist you to figure out how big a sample size you need from your website visitors. A small sample size would not allow for enough traffic from different segments. You may miss a few portions, which could have an impact on your test results. With a bigger sample size, you have a better chance of accounting for all of the variables that influence the test.

A/A testing can be used to teach a customer the importance of completing a test with a sufficient number of people before concluding that a variation outperforms the original. Besides, A/A testing reveals how A/B testing can be deceptive if it is not taken seriously. It's also a good technique to find any issues in the tracking system.

Further in this article, you will get a general overview of the reason why organizations need to test for identical pages with the help of A/B and A/A testing.

Reason for Testing Identical Pages

Before starting an A/B or multivariate test, companies may want to measure on-page conversions where the A/A test is being conducted to track the number of conversions and determine the baseline conversion rate.

In most other circumstances, the A/A test is a technique of double-checking the A/B testing software's usefulness and correctness. Check to verify if the software displays a statistically significant difference between the control and variation (>95% statistical significance). There's a problem if the software says there's a statistically significant difference. Professionals should double-check that the program is working properly on the website or mobile app.

Now that you have a basic understanding of A/A testing and why organizations perform these tests, let us discuss some of the things that organizations should know when it comes to A/A testing.

Things to consider before performing A/A Tests

It is extremely important to remember that finding a conversion rate difference between identical test and control sites is always a possibility when doing an A/A test. This isn't always a negative reflection on the A/B testing platform, as testing usually involves some degree of chance.

Keep in mind that the statistical significance of your data is a likelihood, not a certainty when doing any A/B test. Even with a 95% statistical significance level, there's a 1 in 20 possibility that the findings you're seeing are due to chance. Because the underlying fact is that there isn't one to find, your A/A test should show that the conversion improvement between the control and variation is statistically inconclusive in most circumstances.

Let’s understand how A/A tests can be performed and executed by professionals.

How to Perform A/A Tests?

Performing an A/A test is similar to running A/B tests, except that the two groups of users for each variation are chosen at random and provide an exact experience.

Mentioned below is the process that needs to be followed while performing the A/A test:

• Users are given two groupings of high-traffic web pages that are completely identical.

• Both of these groups have had a comparable user experience.

• The KPI for the two groups is supposed to be the same as well. If the KPIs don't match, it's time to figure out the reason why.

Furthermore, companies should also interface their A/B testing tool with the analytics so that you can compare the conversions and revenue reported by the testing tool to the conversions and revenue reported by analytics - they should match.

To conduct an A/A test or not is a question that elicits a variety of responses. Some companies believe A/A testing to be a complete loss and waste of time and resources.

With everything having its pros and cons, A/A tests also come with a few problems. Let us briefly read about them.

A/A Test Issues

In a nutshell, A/A testing has two primary drawbacks:

1. Any experimental setup contains an element of unpredictability

The major reason for doing an A/A test, as mentioned previously in the text, is to assess the accuracy of a testing tool. What if, on the other hand, you discover a difference between control and variation conversions? Do you usually call it a bug in the A/B testing software?

The issue with A/A testing (for lack of a better term) is that there is always some element of chance involved. In other circumstances, statistical significance is attained solely by chance, implying that the difference in conversion rates between A and its identical variant is probabilistic rather than absolute. For example, assume you open two identical stores in the same neighborhood. There is most likely a variation in the findings provided by the two due to chance or randomization. It also does not always imply that the A/B testing platform is ineffective.

2. The need for a big sample size

One issue with A/A testing is that it can take a long time. When comparing similar copies, a large sample size is required to determine whether A is favored over its identical counterpart. As a result, this will require an excessive amount of time. An optimization program's sole objective is to decrease time, resource, and financial waste. They argue that while doing an A/A test is not inherently bad, there are better ways to spend your testing time. It's vital to start a lot of tests, but it's even more crucial to finish them and learn something meaningful from them. A/A tests can take time away from real testing.

Other methods can be used by organizations instead of A/A testing. We will read about that further in this blog.

A/A Testing Alternatives

Some experts argue that A/A testing is unproductive since it wastes time that could be spent doing true A/B tests. Others, on the other hand, believe that performing a health check on your A/B testing platform is critical. However, A/A testing alone is insufficient to determine whether one testing methodology is preferable to another. There are various factors to consider before making a vital business choice, such as purchasing a new tool for A/B testing.

Following are some of the factors that need to be considered before the purchase of new tools:

• Will the testing platform be able to integrate with my web analytics tool so that I may slice and dice the test results for further information?

• Will the technology allow me to isolate and test only specific audience groups that are crucial to my business?

• Will the tool allow me to direct 100% of my visitors to a winning variety right away? This capability is useful for more complex radical redesign testing in which standardizing on variation takes some time. You can reap the benefits of the improvement while the page is being generated permanently in your CMS if your testing technology supports immediate 100% allocation to the winning variation.

• Is there a method to obtain both quantitative and qualitative information on-site visits that can be utilized to come up with new test ideas on the testing platform? Heatmaps, scroll maps, visitor records, exit surveys, page-level surveys, and visual form funnels are examples of such tools. Is it possible to integrate these services with third-party tools if the testing platform does not have them built-in?

• Is it possible to change the tool's appearance? Does the tool allow you to permanently deliver these distinct experiences for different audience segments if test results are segregated and it is identified that one sort of material works best for one section and another piece of content works best for a different segment?

However, some experts or individuals would choose to use alternatives such as triangulating data over an A/A test. You'll have two sets of performance statistics to compare if you use this approach. Use one analytics platform as a baseline against which all other results may be compared to see if anything is amiss or needs to be addressed.

Then there's the counter-argument: why conduct an A/A test when an A/B test can yield more useful results? This allows you to compare two identical versions while also putting the B variation through its paces.

Conclusion

When a company decides to install a new testing software program, it must conduct a thorough review of the tool. Some businesses employ A/A testing to evaluate the efficacy of a tool before determining whether to build it in-house or purchase it. A/A testing, like the other tips in this piece, allows for customization and segmentation. It can also assist in determining whether the software program is suitable for implementation.